AWS SageMaker and SingleStore Helios enable organizations to deploy machine learning models directly into their operational databases, reducing latency and cost. By leveraging the power of in-database analytics, companies can accelerate model execution against real-time data streams, streamlining their data science environments and improving overall performance. With Singlestore Helios, users can build and deploy supervised learning models within a unified store, reducing the complexity and overhead associated with operationalizing machine learning models using SageMaker. The platform also supports high concurrency and petabyte-scale analytics, making it an ideal solution for large-scale machine learning deployments.